Identification of risk factors for hypertension and tachycardia upon dexmedetomidine discontinuation

Severe Hypertensive Response to Atropine Therapy for Bradycardia Associated with Dexmedetomidine: Case Report and Literature Review

Dexmedetomidine is a selective and potent α2-adrenoceptor agonist used for sedation, analgesia, and anxiolysis, with minimal respiratory depression; therefore, it is widely used in clinical practice. Transient hypertension has been reported to be an indication for the use of dexmedetomidine. The authors report three female patients who experienced hypertensive crisis when used atropine to treat bradycardia caused by dexmedetomidine. The transient hypertension is a relatively common side effect of dexmedetomidine, hypertensive crisis seen with coadministration of atropine is much less frequently reported. This is the first report to describe the use of atropine to treat bradycardia induced by dexmedetomidine, which may cause severe hypertension in female patients. They discuss the reason for and treatment of hypertension caused by administration of atropine and dexmedetomidine together and review the relevant literature.

Management of Incidental Uncontrolled Elevation of Intraoperative Blood Pressure: A Comparative Study on Nitroglycerine Versus Dexmedetomidine Infusion

Background

Uncontrolled elevation of intraoperative blood pressure can result in deleterious effects with increased risk of morbidity and mortality.

Objectives

We aimed to compare nitroglycerine infusion with dexmedetomidine infusion in controlling accidental intraoperative uncontrolled hypertension.

Methods

This comparative study was conducted on 73 hypertensive patients undergoing cancer surgeries who experienced uncontrolled intraoperative hypertension. The data of 38 patients were retrieved from the medical records for the nitroglycerine group and 35 patients were prospectively enrolled for the dexmedetomidine group. Group N received nitroglycerine infusion (0.3 - 1 µg/kg/min), while group D received dexmedetomidine infusion (0.2 - 0.7 µg/kg/h).

Results

Both groups were comparable regarding their demographic data and clinical characteristics. Systolic, diastolic, and mean arterial pressure (MAP) values were significantly lower in group N compared to group D during the period between 60 and 120 minutes intraoperatively (P < 0.001). Heart rate values were significantly lower in group D than in group N (P < 0.001). Postoperative sedation scores were better for group D with lower analgesic requirements (P < 0.001).

Conclusions

Dexmedetomidine infusion can be used to manage the uncontrolled intraoperative elevation of blood pressure in selected patient population.

DHDIP: An interpretable model for hypertension and hyperlipidemia prediction based on EMR data

BACKGROUND AND OBJECTIVE

Traditional hypertension and hyperlipidemia prediction models suffer from uneven modeling data sources, small sample sizes, and a lack of uniform standards for the index system, resulting in the model failing to fulfill clinical applications. To address this issue, this work will offer DHDIP, an interpretable hypertension and hyperlipidemia prediction model based on EMR data.

METHODS

First, we will select massive high-dimensional, unstructured EMR data as a unified modeling data source, and propose a pre-processing algorithm for EMR data to solve the problem that EMR data cannot be directly processed by machine learning algorithms. Second, a variety of mainstream models such as XGBoost, CatBoost, and RandomForest are selected for modeling, and the best adaptation algorithms are identified by performance comparison. Finally, the SHAP framework was introduced into the DHDIP model, thus identifying the main factors contributing to hypertension and hyperlipidemia, effectively enhancing the interpretability of the model.

RESULTS

The DHDIP model's MSE value is 0.0285, and its LOSS value is 0.0054, both of which are better than previous studies.

CONCLUSION

The model balances performance and interpretability. Multi-objective learning allows for a more thorough analysis and prediction of the condition, which not only lowers the cost of disease prediction but also aids physicians in clinical diagnosis. In addition, the datasets and source code are available from this link: https://github.com/Xiaoyao-Jia/DHDIP.